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Experimental Studies of Evolutionary Dynamics in Microbes

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Experimental Studies of Evolutionary Dynamics in Microbes Review Experimental Studies of Evolutionary Dynamics in Microbes 1,2 1,2 1,2,3, Ivana Cvijovic, Alex N. Nguyen Ba, and Michael M. Desai * Evolutionary dynamics in laboratory microbial evolution experiments can be Highlights surprisingly complex. In the past two decades, observations of these dynamics Laboratory evolution experiments show that microbial populations con- have challenged simple models of adaptation and have shown that clonal tinue to rapidly adapt even over long fi interference, hitchhiking, ecological diversi cation, and contingency are timescales in simple constant environ- ments. Beneficial mutations remain widespread. In recent years, advances in high-throughput strain maintenance abundant and populations typically and phenotypic assays, the dramatically reduced cost of genome sequencing, do not reach a fitness peak. and emerging methods for lineage barcoding have made it possible to observe Regular patterns of ‘global’ epistatic evolutionary dynamics at unprecedented resolution. These new methods can interactions as well as idiosyncratic now begin to provide detailed measurements of key aspects of fitness interactions between individual muta- landscapes and of evolutionary outcomes across a range of systems. These tions lead to evolutionary contingency. It is unclear to what extent this con- measurements can highlight challenges to existing theoretical models and tingency affects the predictability and guide new theoretical work towards the complications that are most widely repeatability of evolution. important. Spontaneous ecological diversification is common, even in simple environ- Surprising Complexity in Simple Experiments mental conditions that were explicitly For many decades, evolutionary adaptation in microbial populations was thought to proceed by designed to minimize this possibility. ‘ ’ fi periodic selection , where individual bene cial mutations arise sequentially and either go Ecological and evolutionary dynamics extinct or fix in independent selective sweeps [1]. In this picture, evolution is relatively simple: often occur on similar timescales. mutations arise randomly and then fix or go extinct at a rate that is commensurate with their Measurements of the pleiotropic individual selective effect. Our ability to predict how a population should evolve is then limited effects of individual mutations on fit- fi only by our knowledge of the biological details of that speci c system (i.e., the potential ness across a range of environmental conditions reveal diverse statistical mutations and their corresponding mutation rates and selective effects, the biological patterns of pleiotropy in different sys- environment in Box 1). tems. These patterns interact with evo- lutionary dynamics to determine how Beginning in the late 1990s, however, observations of surprising complexity in microbial populations specialize as they adapt. evolution experiments provided convincing evidence rejecting this standard ‘periodic selection’ picture. Instead, careful observations of rates of fitness increase [2–4] and changes in the frequencies of genetic markers over time [5–9] pointed to widespread signatures of clonal interference (see Glossary) and hitchhiking. These complications make it much harder to 1 predict how evolution will act: we are limited not only by our knowledge of the biological details Department of Organismic and Evolutionary Biology, Harvard but also by our lack of understanding of the evolutionary dynamics themselves. The basic University, Cambridge, MA 02138, fi dif culty is that many interacting loci across the genome are hopelessly intertwined: evolution USA 2 cannot change the frequencies of alleles at one locus without simultaneously affecting alleles at FAS Center for Systems Biology, Harvard University, Cambridge, MA many other linked loci (Figure 1, Key Figure). In these settings, we cannot rely on 02138, USA well-established models of evolution at individual loci to predict evolutionary dynamics. 3 Department of Physics, Harvard University, Cambridge, MA 02138, USA Inspired in large part by these experiments, there is now a thriving theoretical community bringing methods from statistical physics and applied mathematics to the study of evolutionary ‘ ’ dynamics in these rapidly evolving populations. This has led to many advances in our analytical *Correspondence: understanding of the effects of clonal interference and other forms of linked selection [10]. [email protected] (M.M. Desai). Trends in Genetics, September 2018, Vol. 34, No. 9 https://doi.org/10.1016/j.tig.2018.06.004 693 © 2018 Published by Elsevier Ltd. Box 1. Key Determinants of Evolutionary Dynamics Glossary Evolutionary dynamics are influenced by a number of different factors. One class of factors involves the physiology of Clonal interference: competition specific organisms in particular environmental contexts. We refer to these as the biological environment; they determine between multiple different (and how selection acts on different genotypes. Another class of factors determines how genetic variation arises and how it is typically beneficial) mutations that are inherited. We refer to these as the population-genetic environment; they determine how genetic drift operates, constrain segregating simultaneously within the how mutations move between haplotypes, and determine which organisms compete and interact. Of course, the population. distinction between the biological and population-genetic environment is somewhat arbitrary. DNA barcode: a DNA sequence that ‘barcodes’ a strain. This often Examples of factors in the population-genetic environment include: refers to a naturally occurring population size, N sequenced used for species mutation rate, U identification in ecological recombination rate, R, and the physical structure of the genome applications. In laboratory evolution, spatial structure. barcodes are sometimes instead Examples of factors in the biological environment include: random sequences (often 10– the ‘local’ distribution of mutational effects on fitness, r(s) 30 bp) that are integrated by the the ruggedness of the landscape [how r(s) changes as a result of epistasis] experimenter into a specific genomic pleiotropic effects of mutations location. statistics of environmental change Epistasis: the dependence of ecological opportunities. phenotypic effects of mutations on the genetic background. Fitness landscape: a general mapping between genotype and fitness in a specific environmental However, increasingly high-resolution observations of evolutionary dynamics in laboratory condition. evolution experiments have continued to reveal unexpected complexities that appear to be Flow cytometry: a technique to fl fi crucial to evolutionary dynamics in these systems and call for still further theoretical work. Here measure the uorescence pro les of individual cells in high throughput; we review these recent developments. often used to count differently labeled cells in a population for Studying Evolution without Phenotype applications such as fitness Many studies of adaptation in both natural and laboratory populations are focused primarily on measurements. Hitchhiking: the process by which a understanding phenotypes; the goal is to characterize adaptive changes and to identify the neutral or deleterious allele increases evolutionary processes by which they arose as well as their genetic and molecular basis. in frequency due to linkage to a Experimental studies of evolutionary dynamics focus instead on understanding evolution as a beneficial mutation; can also refer to a weakly beneficial mutation stochastic algorithm. That is, given a particular set of biological details (i.e., the set of mutations increasing in frequency due to that can arise and their corresponding fitness effects in all relevant environments and genetic linkage to a more strongly beneficial fi backgrounds, often referred to as the tness landscape), what will evolution actually do? one. fi What mutations will x with what probabilities? How repeatable is the process and what Pleiotropy: the effect of a mutation on multiple different phenotypes. patterns of genetic diversity will a population display? The focus is on the role of the dynamics in Here, the phenotypes discussed are determining evolutionary outcomes and not on the nature of the adaptive phenotypes or their often fitness effects in different genetic and molecular basis. That is, we aim to study evolution as a process, without reference environments. to the specific phenotypes in question. In principle, given a specific landscape and set of population-genetic parameters, we can address any questions about how evolution acts by implementing computational simulations of the dynamics. However, we cannot possibly measure the fitness landscape in every system we wish to understand. Instead, we hope to be able to identify key principles of evolutionary dynamics that help us understand what general features of landscapes are important and how these features influence evolution across a wide range of systems. With this goal in mind, many theoretical studies have focused on simple, analytically tractable models. Since evolutionary dynamics are inherently random, testing these models involves quantifying the probabilities of different outcomes. This requires highly controlled and replicated experi- ments, which make it possible to identify deviations from existing theoretical predictions that point to important new processes that future theory must account for. For example, classic 694 Trends in Genetics,
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